Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type. Typically, such methods involve introducing additional constraints to generic segmentation approaches. However, we show that this is often inconsistent with respect to common assumptions about the image. The proposed method introduces a new fitting term that is more useful in practice than the Chan–Vese framework. In particular, the idea is to define a term that allows for the background to consist of multiple regions of inhomogeneity. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods.

中文翻译：

---

Chan-Vese重新制定选择性图像分割。

选择性分割涉及通过区分相似类型的对象，合并用户输入以将图像分为前景和背景。通常，此类方法涉及将其他约束条件引入通用分割方法。但是，我们表明，这通常与有关图像的常见假设不一致。所提出的方法引入了一个新的拟合术语，该术语比Chan-Vese框架在实践中更有用。具体而言，该想法是定义一个术语，该术语允许背景由不均匀的多个区域组成。我们提供了替代方法的对比实验结果，以证明所提出方法的优点，从而拓宽了这些方法的可能应用范围。